Air Pollution-Related Mortality in Bangkok: A Time-Series Neural Network Analysis

Abstract

Objective: This study aimed to predict and analyze air pollution-related mortality rates in Bangkok, Thailand, using a comprehensive neural network (NN) model. Objectives included analyzing temporal dynamics, evaluating model effectiveness, and identifying the influential factors. Material and Methods: Daily air quality and mortality data from 2016 to 2020 were used. We employed recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRU) models to capture the complex relationships between six key pollutants and mortality. The best model was selected based on the lowest R2 and mean difference from the Bland–Altman analysis. SHAP feature importance and subgroup analyses for age (0–5, 5–60, and 60+ years) and cause of death (respiratory, circulatory, and other) were conducted. Results: Analysis included 170,612 mortality cases over 1,828 days, with a median=36 daily premature deaths. An LSTM model with a 23-day time lag demonstrated the highest predictive accuracy for overall mortality. Subgroup analysis identified different optimal models; an RNN model was best for the “Older Adult” subgroup, and an LSTM model was best for the “Respiratory” subgroup. For feature importance, relative humidity, particulate Matter (PM2.5), and ozone (O3) were the most influential for overall mortality. The most influential factors for older adults were PM10, PM2.5, and carbon monoxide (CO); for respiratory, PM2.5, nitrogen dioxide (NO2), and PM10 were the most influential. Conclusion: This study demonstrates NN’s potential in predicting air pollution-related mortality rates in Bangkok. Findings highlight the importance of considering temporal dynamics, subgroup-specific characteristics, and the key environmental factors in model development. These data-driven insights can inform public health policies and facilitate targeted interventions to mitigate the health impacts of urban air pollution.